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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 4951–4975 of 9051 papers

TitleStatusHype
Regularized Submodular Maximization at Scale—0
Regularizing Dialogue Generation by Imitating Implicit Scenarios—0
Regular Time-series Generation using SGM—0
Regurgitative Training: The Value of Real Data in Training Large Language Models—0
Reimagining Speech: A Scoping Review of Deep Learning-Powered Voice Conversion—0
Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem—0
Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection—0
Reinforcement-based frugal learning for satellite image change detection—0
Reinforcement Learning based QoS/QoE-aware Service Function Chaining in Software-Driven 5G Slices—0
Reinforcement Learning for Sequence Design Leveraging Protein Language Models—0
Reinforcement Learning from Diffusion Feedback: Q* for Image Search—0
Reinforcement Learning with Human Feedback for Realistic Traffic Simulation—0
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition—0
Relationship-Aware Spatial Perception Fusion for Realistic Scene Layout Generation—0
Relative Variational Intrinsic Control—0
Genealogical transition in the noisy N-Branching Random Walk. How stronger selection may promote genetic diversity—0
Relay Assisted OFDM with Subcarrier Number Modulation in Multi-Hop Cooperative Networks—0
Long-horizon Embodied Planning with Implicit Logical Inference and Hallucination Mitigation—0
Relevance-guided Unsupervised Discovery of Abilities with Quality-Diversity Algorithms—0
Relevance-Promoting Language Model for Short-Text Conversation—0
Relevance Transformer: Generating Concise Code Snippets with Relevance Feedback—0
RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity—0
Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification—0
Remembering What Is Important: A Factorised Multi-Head Retrieval and Auxiliary Memory Stabilisation Scheme for Human Motion Prediction—0
ReMix: Training Generalized Person Re-identification on a Mixture of Data—0
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